Exponential linear units-guided Depthwise separable convolution network with cross attention mechanism for hyperspectral image classification

Abstract

Hyperspectral images (HSI) are more informative than other remote sensing techniques and hence frequently utilized in numerous domains. Convolutional neural network algorithm provides outstanding performance in image processing and currently has established as the main approach in the field of HSI classification. HSI contain numerous channels with various spatial and spectral feature information, and simultaneously contain massive redundant information, leading to dimensional explosion or gradient disappearance in the process of classification. A multilayer network model based on Exponential Linear Units-guided Depthwise Separable Convolution is proposed in this paper to extract both spectral and spatial features from HSI at scales ranging, and the feature maps are then fed into a cross-attention mechanism for weight allocation to improve local feature information and optimize computational resource allocation. Extensive experiments on three well-known hyperspectral datasets are conducted, and the results show that the proposed network model can accurately complete the required HSI classification operation and outperforms other well-established techniques in terms of computing efficiency.